Kapouleas I
Department of Radiology, University of Pennsylvania, Philadelphia 19104-6021.
Comput Methods Programs Biomed. 1990 May;32(1):17-35. doi: 10.1016/0169-2607(90)90082-k.
A new approach to automating radiologic diagnosis is described and tested in a system that locates multiple sclerosis lesions in magnetic resonance human brain images. This approach uses a step-by-step procedure, where the most obvious features in the images are identified first, and used to calibrate the application of the next step, until the desired features are identified. The approach stresses testing on a large number of images of the same type, and exploiting the special characteristics of this type of images. The system was designed with the variations of the various image parameters in mind, to ensure its reliability. New low-level methods of high reliability have been developed for segmenting images. A new geometric modeling method which tolerates the variability of biological structures is used to encode anatomic knowledge. The system and its components have been tested on 1132 images from 17 patients, with very good results.
本文描述了一种用于放射学诊断自动化的新方法，并在一个定位磁共振人脑图像中多发性硬化症病变的系统中进行了测试。该方法采用逐步程序，首先识别图像中最明显的特征，并用于校准下一步的应用，直到识别出所需特征。该方法强调在大量相同类型的图像上进行测试，并利用此类图像的特殊特性。该系统在设计时考虑了各种图像参数的变化，以确保其可靠性。已开发出具有高可靠性的新的低层次图像分割方法。一种能够容忍生物结构变异性的新几何建模方法被用于编码解剖学知识。该系统及其组件已在来自17名患者的1132幅图像上进行了测试，结果非常好。
